Type 1 diabetes mellitus (T1DM) is a chronic metabolic disease characterized by T-cell-mediated autoimmune destruction of the insulin-secreting β-cells of the endocrine pancreas. Absolute insulin deficiency occurs, leading to hyperglycemia. Current regimens for treating type 1 diabetes in clinical practice are mainly based on injections of subcutaneous insulin either continuously or several times daily in dosages determined by intermittent measurements of blood glucose levels. The blood glucose level (or blood sugar concentration) comprises or represents the amount of glucose in the blood of an animal or human being and is measured in terms of molar concentration (mmol/L) according to the International Standard or mass concentration (mg/dL) according to the US Standard. The latter is used in the following description.
The Diabetes Control and Complications Trial (DCCT) Research Group published their results on the effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus in New England Journal Medical, 1993, 329(14), pages 977-986, which demonstrated that intensive management using this treatment algorithm reduced micro-vascular complications by 50-76%. However, this was at the expense of increased time spent in hypoglycemia, especially at hemoglobin A1c levels <7.5%.
In other studies, intensive management resulted in subjects spending 30% of the day with glucose values >10 mM (180 mg/dl) and >2 hours/day in hypoglycemia, often at night as described by B. W. Bode, S. Schwartz, H. A. Stubbs, J. E. Block, “Glycemic characteristics in continuously monitored patients with type 1 and type 2 diabetes: normative values”, Diabetes Care, 2005, 28(10), pages 2361-6. This led to automatic blood glucose control.
An automatic closed-loop system was described by R. Hovorka, “Continuous glucose monitoring and closed loop systems.” Diab Med., 2005, 23(1), pages 1-12, which provided the potential to improve hemoglobin A1c while avoiding hypoglycemia. Many researchers have found that this type of system requires continuous glucose measurement, a control device, and a pump for insulin delivery. See for example, B. Kovatchev, S. Anderson, L. Heinemann, and W. Clarke, “Comparison of the numerical and clinical accuracy of four continuous glucose monitors”, Diabetes Care, 2008, 31, pages 1160-1164; B. W. Bequette, “A critical assessment of algorithms and challenges in the development of a closed—loop artificial pancreas”, Diabetes Technol Ther., 2005, 7, page 28; or R. Rowe, “Insulin pump therapy”, Diabetic Medicine, 2001, 18, pages 4-5.
Closed-loop control algorithms used in the context of an artificial pancreas (AP) have been mainly based on classical control engineering techniques, and dominated by Proportional Integral Derivative (PID) control as described by G. M. Steil, K. Rebrin, C. Darwin, F. Hariri and M. F. Saad, “Feasibility of automating insulin delivery for the treatment of type 1 diabetes”, Diabetes, 2006, 55, pages 3344-3350, and Model Predictive Control (MPC) as in R. Hovorka, “Continuous glucose monitoring and closed loop systems”, Diab Med., 2005, 23(1), pages 1-12, or F. H. El—Khatib, S. J. Russell, D. M. Nathan, R. G. Sutherlin, and E. R. Damiano, “A bihormonal closed—loop artificial pancreas for type 1 diabetes”, Sci Transl Med., 2010, 2(27), page 27.
Other approaches, based on empiric knowledge, have also been proposed through the use of artificial intelligence techniques as described in E. Atlas, R. Nimri, S. Miller, E. A. Grunberg and M. Phillip, “MD-Logic Artificial Pancreas System—A pilot study in adults with type 1 diabetes”, Diabetes Care, 2010, 33(5), pages 1072-1076 and D. Campos-Delgado, M. Hernandez-Ordonez, R. Fermat, and A. Gordillo-Moscoso, “Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route”, IEEE Trans Biomed Eng., 2006, 53(11), pages 2201-2210.
Bio-inspired approaches for solving medical problems have been motivated by the belief that nature has evolved over millions of years to carry out its tasks more optimally and efficiently. Therefore replicating the functionality of the human body can lead to a system with greater physiological function, which the body can accept. Bio-inspired technologies for artificial organs have already been successfully implemented in different medical areas including cochlear implants, retinal implants, and vestibular implants. C. Toumazou, J. A. Georgiou, “A 126 μW cochlear chip for a totally implantable system”; IEEE Journal Solid State Circuits, 2005, 40(2), pages 430-43 describes modelling the way the basilar membrane of the cochlear behaves and therefore can restore hearing. P. Degenaar, N. Grossman, M. A. Memon, et al, “Optobionic vision—a new genetically enhanced light on retinal prosthesis”; J Neural Eng., 2009, 6, pages 1741-2552, describe retinal implants that model the local processing which occurs in the neuronal circuits of the retina to derive extremely fast and low power image restoration. Constandinou, T. G., Georgiou, J., Toumazou, C., “A neural implant ASIC for the restoration of balance in individuals with vestibular dysfunction”, Circuits and Systems, 2009, ISCAS 2009. IEEE International Symposium on, pages 641-644, 24-27 May 2009 presented vestibular implants that replicate the inertial measurements of the human body to restore balance. Given these successes, it has been considered there may be some benefit to consider a control strategy for controlling blood glucose based on the biological function of the pancreas.
Bio-inspired control systems can be used to replicate the functionality of the pancreas and provide more physiological solutions, especially in the area of prosthetic organs. The bio-inspired approaches for blood glucose control is based on the biphasic nature of insulin secretion from the beta-cells in the pancreas, which depends on the type and magnitude of the glucose stimulus as described by A. Caumo and L. Luzi, “First-phase insulin secretion: does it exist in real life? Considerations on shape and function”; AJP—End., 2004, 287(3), E371-E385. Both animal and human studies indicate that the first-phase insulin response to intravenous glucose has beneficial effects on the regulation of glucose metabolism. In particular, the first-phase has a profound and long-term inhibitory effect on hepatic glucose production. Likewise, the early insulin response to ingested glucose is an important determinant of prandial glucose tolerance.
FIG. 1 is an illustration of a graph showing an example of biphasic insulin secretion by a β-cell corresponding to a glucose stimulus from a meal. The y-axis of the upper graph represents plasma glucose (mg/dL) and the y-axis of the lower graph represents insulin secretion (ug/min), where the x-axis on both graphs represents time in minutes. The graphs illustrate the presence of a sharp first-phase due to the rapidly changing glucose concentration (i.e. derivative effect) and afterwards, a second-phase represented by sustained insulin release. Replicating the β-cell behaviour in response to a glucose stimulus may assist in controlling blood glucose concentration in T1DM subjects.
G. M. Steil et. al., “Modeling b-Cell Insulin Secretion-Implications for Closed-Loop Glucose Homeostasis”, Diabetes Technology & Therapeutics, 2003, 5(6) describes using a bio-inspired approach for blood glucose control, which used a minimal model of insulin secretion, previously described by E. Breda et. al, “Insulin release in impaired glucose tolerance: oral minimal model predicts normal sensitivity to glucose but defective response times”; Diabetes, 2002, 51(1), S227-S233.
This simple model represents the insulin secretion by decomposing it into a static rate of secretion, which basically depends on the plasma glucose concentration, and a dynamic secretion rate (second phase), which depends on the rate of change of plasma glucose concentration (first phase). Steil et. al. compared the minimal model of insulin secretion with a PID controller and concluded that both were able to fit experimental data. However, the insulin secretion model was less stable than the PID controller under closed-loop conditions due to the simplification of the bio-inspired model.
Oliver et al., “A Benchtop Closed-Loop System Controlled by a Bio-Inspired Silicon Implementation of the Pancreatic β-Cell”, Journal of Diabetes Science and Technology, 2009, 3(6), used a model of the electrical activity of β-cell for closed-loop glucose control and implemented a semiconductor ASIC, which formed a “silicon” beta cell. However, the model lacked sufficient detail of insulin release.
Some recent developments of mathematical models of β-cell physiology are described by M. G. Pedersen et. al., “Intra-and inter-islet synchronization of metabolically driven insulin secretion”; Biophys J., 2005, 89(1), pages 107-119; A. Bertuzzi et. al., “Insulin granule trafficking in beta-cells: mathematical model of glucose-induced insulin secretion”; Am J Physiol Endocrinol Metab., 2007, 293, E396-E409; Yi-der Chen et. al., “Identifying the Targets of the Amplifying Pathway for Insulin Secretion in Pancreatic β-Cells by Kinetic Modelling of Granule Exocytosis”, Biophysical Journal, 2008, 95(5), pages 2226-2241, and M. G. Pedersen et. al., “Cellular modeling: insight into oral minimal models of insulin secretion”, Am J Physiol Endocrinol Metab., 2010, 298, E597-E601. These documents describe the glucose-induced insulin release at a molecular level and have led to a new class of bio-inspired glucose control algorithms.
However, currently available technologies for continuous glucose monitoring (CGM) and continuous insulin infusion (CSII) use the subcutaneous (s.c.) route, which despite the clear advantage of being minimally invasive, are far from being physiological, and consequently non-optimal. The s.c. route introduces some extra difficulties to the glucose control in the form of time delays in the glucose sensing and insulin action, measurement errors and higher variability. Time delays are introduced by glucose sensing (up to 15 minutes) and insulin action (15-20 minutes). The variability of these delays can be high and the accuracy of the current s.c. continuous glucose sensors are far from being optimal with mean absolute differences of up to 20%, especially in hypoglycaemia, which is the critical state to avoid. It is desirable to devise an improved control strategy for controlling insulin pumps that overcomes these limitations.